Motivated by analyzing long-term physiological time series, we design a robust and scalable spectral embedding algorithm, coined the algorithm RObust and Scalable Embedding via LANdmark Diffusion (ROSELAND). The key is designing a diffusion process on the dataset, where the diffusion is forced to interchange on a small subset called the landmark set. In addition to demonstrating its application to spectral clustering and image segmentation, the algorithm is applied to study the long-term arterial blood pressure waveform dynamics during a liver transplant operation lasting for 12 hours long.

中文翻译：

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通过地标扩散进行鲁棒且可扩展的流形学习，用于长期医学信号处理

通过分析长期的生理时间序列，我们设计了一种鲁棒且可扩展的频谱嵌入算法，该算法是通过LANdmark扩散（ROSELAND）提出的RObust和可扩展嵌入算法。关键是在数据集上设计扩散过程，在该过程中，扩散被迫在称为地标集的小子集上互换。除了演示其在频谱聚类和图像分割中的应用外，该算法还用于研究持续12小时的肝脏移植手术期间的长期动脉血压波形动态。